PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming. (arXiv:2206.14234v2 [math.OC] UPDATED)

In deterministic optimization, it is typically assumed that all problem
parameters are fixed and known. In practice, however, some parameters may be a
priori unknown but can be estimated from historical data. A typical
predict-then-optimize approach separates predictions and optimization into two
stages. Recently, end-to-end predict-then-optimize has become an attractive
alternative. In this work, we present the PyEPO package, a PyTorchbased
end-to-end predict-then-optimize library in Python. To the best of our
knowledge, PyEPO (pronounced like pineapple with a silent "n") …

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